计算机工程与应用2018，Vol.54Issue(7)：36-43,8.DOI:10.3778/j.issn.1002-8331.1801-0013
结合K近邻的改进密度峰值聚类算法
Improved density peaks clustering algorithm combining K-Nearest Neighbors
摘要
Abstract
Concerning the problem that Density Peaks Clustering(DPC)algorithm has poor performance on the datasets with high dimension,noise and complex structure,an Improved Density Peaks Clustering Algorithm(IDPCA)combining K-Nearest Neighbors is proposed.Firstly,a new definition of local density is proposed to describe the distribution of the spatial samples.Secondly,the concept of core point is introduced and a global search allocation strategy is designed based on K-Nearest Neighbors thought to classify the unassigned K-Nearest Neighbors of core points correctly,which acceler-ates the clustering speed.Thirdly,a statistical learning allocation strategy is developed,by using the weighted K-Nearest Neighbors'information of the unassigned points to calculate the probability of them being assigned to each local cluster, which improves the clustering quality effectively.Finally,compared with DPC and other three classical clustering methods on 21 test datasets including synthetic and real-world datasets, the experimental results show that IDPCA outperforms them on four different evaluation indexes.关键词
数据挖掘/聚类算法/局部密度/密度峰值/K近邻Key words
data mining/clustering algorithm/local density/density peaks/K-Nearest Neighbors分类
信息技术与安全科学引用本文复制引用
薛小娜,高淑萍,彭弘铭,吴会会..结合K近邻的改进密度峰值聚类算法[J].计算机工程与应用,2018,54(7):36-43,8.基金项目
国家自然科学基金(No.91338115) （No.91338115）
高等学校学科创新引智基地"111"计划(No.B08038). （No.B08038）
